Abstract
Continuous aggregation queries with a tolerable error threshold have many applications in sensor networks. Since the communication cost is important in the lifetime of sensor networks, there have been a few methods to reduce the communication cost for continuous aggregation queries having a tolerable error threshold. In previous methods, the error threshold in each node is periodically adjusted based on the global statistics collected in the central site that are obtained from all the nodes in the network. These methods require that users specify a few parameters, e.g., adjustment period. However, determination of these parameters by users, in practice, is very difficult and undesirable for sensor network applications demanding unattended operations in dynamically changing environments. In this paper, we propose a new in-network data aggregation protocol, called the Distributed Adaptive Filtering (DAF) protocol. It works in a distributed manner and proceeds adaptively in the sense that the filtering condition in each node is adaptively changed by using only local information. It does not require user parameters that are used in the previous method. We show through various experiments that the proposed method outperforms other existing methods.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
I.F. Akyildiz, W. Su, Y. Sankarasubramaniam, and E. Cayirci, “A survey on sensor networks,” IEEE Communication Magazine, pp. 102–114, 2000.
A. Cerpa and D. Estrin, “ASCENT: Adaptive self-configuring sEnsor networks topologies,” IEEE Transactions on Mobile Computing, vol. 3, no. 3, pp. 272–285, 2004.
D. Culler, D. Estrin, and M. Srivastava, “Guest editors’ introduction: Overview of sensor networks,” IEEE Computer, vol. 37, no. 8, pp. 41–49, 2004.
A. Deligiannakis, Y. Kotidis, and N. Roussopoulos, “Hierarchical in-network data aggregation with quality guarantees,” in Proceedings of International Conference on Extending Database Technology, 2004, pp. 658–675.
M. Demirbas and H. Ferhatosmanoglu, “Peer-to-peer spatial queries in sensor networks,” in Proceedings of IEEE International Conference on Peer-to-Peer Computing, 2003.
A. Deshpande, C. Guestrin, and S. Madden, “Using probabilistic models for data management in acquisitional environments,” in Proceedings of CIDR, 2005, pp. 317–328.
A. Deshpande, C. Guestrin, S. Madden, J. Hellerstein, and W. Hong, “Model driven data acquisition in sensor networks,” in Proceedings of International Conference on Very Large Data Bases, 2004, pp. 588–599.
D. Estrin, R. Govindan, J.S. Heidemann, and S. Kumar, “Next century challenges: Scalable coordination in sensor networks,” in Proceedings of ACM Mobile Computing and Networking, ACM Press, 1999, pp. 263–270.
B. Greenstein, D. Estrin, R. Govindan, S. Ratnasamy, and S. Shenker, “DIFS: A distributed index for features in sensor networks,” in Proceedings of IEEE International Workshop on Sensor Network Protocols and Applications, 2003.
C. Intanagonwiwat, R. Govindan, and D. Estrin, “Directed diffusion: A scalable and robust communication paradigm for sensor networks,” in Proceedings of ACM Mobile Computing and Networking, ACM Press, 2000, pp. 56–67.
A. Jain, E.Y. Chang, and Y.-F. Wang, “Adaptive stream resource management using kalman filters,” in Proceedings of ACM International Conference on Management of Data, ACM Press, 2004, pp. 11–22.
J. Kulik, W. Rabiner, and H. Balakrishnan, “Adaptive protocols for information dissemination in wireless sensor networks,” in Proceedings of ACM Mobile Computing and Networking, ACM Press, 1999.
P. Levis, N. Lee, M. Welsh, and D. Culler, “TOSSIM: Accurate and scalable simulation of entire tinyOS applications,” in Proceedings of ACM SenSys, ACM Press, 2003.
S. Madden et al., “Tag: A tiny aggregation service for ad hoc sensor networks,” in Proceedings of OSDI, 2002.
S. Madden, M.J. Franklin, J.M. Hellerstein, and W. Hong, “The design of an acquisitional query processor for sensor networks,” in Proceedings of ACM International Conference on Management of Data, ACM Press, 2003, pp. 491–502.
S.R. Madden, M.J. Franklin, J.M. Hellerstein, and W. Hong, “TinyDB: An acquisitional query processing system for sensor networks,” ACM Transactions on Database Systems, vol. 30, no. 1, 2005.
C. Olston, J. Jiang, and J. Widom, “Adaptive filters for continuous queries over distributed data streams,” in Proceedings of ACM International Conference on Management of Data, ACM Press, 2003, pp. 563–574.
R. Szewczyk, E. Osterweil, J. Polastre, M. Hamilton, A. Mainwaring, and D. Estrin, “Habitat monitoring with sensor networks,” Communications of the ACM, vol. 47, no. 6, pp. 34–40, 2004.
J. Winter, Y. Xu, and W.-C. Lee, “Energy efficient processing of K nearest neighbor queries in location-aware sensor networks,” in Proceedings of International Conference on Mobile and Ubiquitous Systems: Networking and Services, IEEE Computer Society, Washington, DC, USA, 2005, pp. 281–292.
Y. Yao and J. Gehrke, “The cougar approach to in-network query processing in sensor networks,” ACM SIGMOD Record, vol. 31, no. 3, pp. 9–18, 2002.
Y. Yao and J. Gehrke. “Query processing for sensor networks,” in Proceedings of Conference on Innovative Data Systems Research, 2003.
F. Zhao and L. Guibas, Wireless Sensor Networks: An Information Processing Approach. Morgan Kaufmann Publishers, 2004.
Author information
Authors and Affiliations
Corresponding author
Additional information
Recommended by:
Ahmed Elmagarmid
Rights and permissions
About this article
Cite this article
Hyun, D., Park, N.J., Son, J.H. et al. Efficient processing of aggregation queries in sensor networks. Distrib Parallel Databases 20, 171–197 (2006). https://doi.org/10.1007/s10619-006-0133-0
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10619-006-0133-0